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All of Us: Release of Nearly 100,000 Whole Genome Sequences Sets Stage for New Discoveries

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Diverse group of cartoon people with associated DNA

Nearly four years ago, NIH opened national enrollment for the All of Us Research Program. This historic program is building a vital research community within the United States of at least 1 million participant partners from all backgrounds. Its unifying goal is to advance precision medicine, an emerging form of health care tailored specifically to the individual, not the average patient as is now often the case. As part of this historic effort, many participants have offered DNA samples for whole genome sequencing, which provides information about almost all of an individual’s genetic makeup.

Earlier this month, the All of Us Research Program hit an important milestone. We released the first set of nearly 100,000 whole genome sequences from our participant partners. The sequences are stored in the All of Us Researcher Workbench, a powerful, cloud-based analytics platform that makes these data broadly accessible to registered researchers.

The All of Us Research Program and its many participant partners are leading the way toward more equitable representation in medical research. About half of this new genomic information comes from people who self-identify with a racial or ethnic minority group. That’s extremely important because, until now, over 90 percent of participants in large genomic studies were of European descent. This lack of diversity has had huge impacts—deepening health disparities and hindering scientific discovery from fully benefiting everyone.

The Researcher Workbench also contains information from many of the participants’ electronic health records, Fitbit devices, and survey responses. Another neat feature is that the platform links to data from the U.S. Census Bureau’s American Community Survey to provide more details about the communities where participants live.

This unique and comprehensive combination of data will be key in transforming our understanding of health and disease. For example, given the vast amount of data and diversity in the Researcher Workbench, new diseases are undoubtedly waiting to be uncovered and defined. Many new genetic variants are also waiting to be identified that may better predict disease risk and response to treatment.

To speed up the discovery process, these data are being made available, both widely and wisely. To protect participants’ privacy, the program has removed all direct identifiers from the data and upholds strict requirements for researchers seeking access. Already, more than 1,500 scientists across the United States have gained access to the Researcher Workbench through their institutions after completing training and agreeing to the program’s strict rules for responsible use. Some of these researchers are already making discoveries that promote precision medicine, such as finding ways to predict how to best to prevent vision loss in patients with glaucoma.

Beyond making genomic data available for research, All of Us participants have the opportunity to receive their personal DNA results, at no cost to them. So far, the program has offered genetic ancestry and trait results to more than 100,000 participants. Plans are underway to begin sharing health-related DNA results on hereditary disease risk and medication-gene interactions later this year.

This first release of genomic data is a huge milestone for the program and for health research more broadly, but it’s also just the start. The program’s genome centers continue to generate the genomic data and process about 5,000 additional participant DNA samples every week.

The ultimate goal is to gather health data from at least 1 million or more people living in the United States, and there’s plenty of time to join the effort. Whether you would like to contribute your own DNA and health information, engage in research, or support the All of Us Research Program as a partner, it’s easy to get involved. By taking part in this historic program, you can help to build a better and more equitable future for health research and precision medicine.

Note: Joshua Denny, M.D., M.S., is the Chief Executive Officer of NIH’s All of Us Research Program.

Links:

All of Us Research Program (NIH)

All of Us Research Hub

Join All of Us (NIH)


Seeking Consensus on the Use of Population Descriptors in Genomics

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Laptop with research article on Ethnicity and Race. Printer printing a page of cartoon faces
Credit: Ernesto del Aguila III, National Human Genome Research Institute, NIH

Cataloging and characterizing the thousands of genomic variants—differences in DNA sequences among individuals—across human populations is a foundational component of genomics. Scientists from various disciplinary fields compare the variation that occurs within and between the genomes of individuals and groups. Such efforts include attributing descriptors to population groups, which have historically included the use of social constructs such as race, ethnicity, ancestry, and political geographic location. Like any descriptors, these words do not fully account for the scope and diversity of the human species.

The use of race, ethnicity, and ancestry as descriptors of population groups in biomedical and genomics research has been a topic of consistent and rigorous debate within the scientific community. Human health, disease, and ancestry are all tied to how we define and explain human diversity. For centuries, scientists have incorrectly inferred that people of different races reflect discrete biological groups, which has led to deep-rooted health inequities and reinforced scientific racism.

In recent decades, genomics research has revealed the complexity of human genomic variation and the limitations of these socially derived population descriptors. The scientific community has long worked to move beyond the use of the social construct of race as a population descriptor and provide guidance about agreed-upon descriptors of human populations. Such a need has escalated with the growing numbers of large population-scale genomics studies being launched around the world, including in the United States.

To answer this call, NIH is sponsoring a National Academies of Sciences, Engineering, and Medicine (NASEM) study that aims to develop best practices in the use of race, ethnicity, and genetic ancestry in genomics research. The NASEM study is sponsored by 14 NIH institutes, centers, offices, and programs, and the resulting report will be released in February 2023.

Experts from various fields—including genomics, medicine, and social sciences—are conducting the study. Much of the effort will revolve around reviewing and assessing existing methodologies, benefits, and challenges in the use of race and ethnicity and other population descriptors in genomics research. The ad hoc committee will host three public meetings to obtain input. Look for more information regarding the committee’s next public session planned for April 2022 on the NASEM “Race, Ethnicity, and Ancestry as Population Descriptors in Genomics Research” website.

To further underscore the need for the NASEM study, an NIH study published in December 2021 revealed that the descriptors for human populations used in the genetics literature have evolved over the last 70 years [1]. For example, the use of the word “race” has substantially decreased, while the uses of “ancestry” and “ethnicity” have increased. The study provided additional evidence that population descriptors often reflect fluid, social constructs whose intention is to describe groups with common genetic ancestry. These findings reinforce the timeliness of the NASEM study, with the clear need for experts to provide guidance for establishing more stable and meaningful population descriptors for use in future genomics studies.

The full promise of genomics, including its application to medicine, depends on improving how we explain human genomic variation. The words that we use to describe participants in research studies and populations must be transparent, thoughtful, and consistent—in addition to avoiding the perpetuation of structural racism. The best and most fruitful genomics research demands a better approach.

Reference:

[1] Evolving use of ancestry, ethnicity, and race in genetics research—A survey spanning seven decades. Byeon YJJ, Islamaj R, Yeganova L, Wilbur WJ, Lu Z, Brody LC, Bonham VL. Am J Hum Genet. 2021 Dec 2;108(12):2215-2223.

Links:
Use of Race, Ethnicity, and Ancestry as Population Descriptors in Genomics Research (National Academies of Sciences, Engineering, and Medicine)

Language used by researchers to describe human populations has evolved over the last 70 years.” (National Human Genome Research Institute/NIH)

Genomic Variation Program (NHGRI)

[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s institutes and centers to contribute occasional guest posts to the blog as a way to highlight some of the cool science that they support and conduct. This is the third in the series of NIH institute and center guest posts that will run until a new permanent NIH director is in place.]


A Race-Free Approach to Diagnosing Chronic Kidney Disease

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A black woman looking off-screen. Anatomical kidneys appear next to her
Credit: True Touch Lifestyle; crystal light/Shutterstock

Race has a long and tortured history in America. Though great strides have been made through the work of leaders like Dr. Martin Luther King, Jr. to build an equal and just society for all, we still have more work to do, as race continues to factor into American life where it shouldn’t. A medical case in point is a common diagnostic tool for chronic kidney disease (CKD), a condition that affects one in seven American adults and causes a gradual weakening of the kidneys that, for some, will lead to renal failure.

The diagnostic tool is a medical algorithm called estimated glomerular filtration rate (eGFR). It involves getting a blood test that measures how well the kidneys filter out a common waste product from the blood and adding in other personal factors to score how well a person’s kidneys are working. Among those factors is whether a person is Black. However, race is a complicated construct that incorporates components that go well beyond biological and genetic factors to social and cultural issues. The concern is that by lumping together Black people, the algorithm lacks diagnostic precision for individuals and could contribute to racial disparities in healthcare delivery—or even runs the risk of reifying race in a way that suggests more biological significance than it deserves.

That’s why I was pleased recently to see the results of two NIH-supported studies published in The New England Journal of Medicine that suggest a way to take race out of the kidney disease equation [1, 2]. The approach involves a new equation that swaps out one blood test for another and doesn’t ask about race.

For a variety of reasons, including socioeconomic issues and access to healthcare, CKD disproportionately affects the Black community. In fact, Blacks with the condition are also almost four times more likely than whites to develop kidney failure. That’s why Blacks with CKD must visit their doctors regularly to monitor their kidney function, and often that visit involves eGFR.

The blood test used in eGFR measures creatinine, a waste product produced from muscle. For about the past 20 years, a few points have been automatically added to the score of African Americans, based on data showing that adults who identify as Black, on average, have a higher baseline level of circulating creatinine. But adjusting the score upward toward normal function runs the risk of making the kidneys seem a bit healthier than they really are and delaying life-preserving dialysis or getting on a transplant list.

A team led by Chi-yuan Hsu, University of California, San Francisco, took a closer look at the current eGFR calculations. The researchers used long-term data from the Chronic Renal Insufficiency Cohort (CRIC) Study, an NIH-supported prospective, observational study of nearly 4,000 racially and ethnically diverse patients with CKD in the U.S. The study design specified that about 40 percent of its participants should identify as Black.

To look for race-free ways to measure kidney function, the researchers randomly selected more than 1,400 of the study’s participants to undergo a procedure that allows kidney function to be measured directly instead of being estimated based on blood tests. The goal was to develop an accurate approach to estimating GFR, the rate of fluid flow through the kidneys, from blood test results that didn’t rely on race.

Their studies showed that simply omitting race from the equation would underestimate GFR in Black study participants. The best solution, they found, was to calculate eGFR based on cystatin C, a small protein that the kidneys filter from the blood, in place of the standard creatinine. Estimation of GFR using cystatin C generated similarly accurate results but without the need to factor in race.

The second NIH-supported study led by Lesley Inker, Tufts Medical Center, Boston, MA, came to similar conclusions. They set out to develop new equations without race using data from several prior studies. They then compared the accuracy of their new eGFR equations to measured GFR in a validation set of 12 other studies, including about 4,000 participants.

Their findings show that currently used equations that include race, sex, and age overestimated measured GFR in Black Americans. However, taking race out of the equation without other adjustments underestimated measured GFR in Black people. Equations including both creatinine and cystatin C, but omitting race, were more accurate. The new equations also led to smaller estimated differences between Black and non-Black study participants.

The hope is that these findings will build momentum toward widespread adoption of cystatin C for estimating GFR. Already, a national task force has recommended immediate implementation of a new diagnostic equation that eliminates race and called for national efforts to increase the routine and timely measurement of cystatin C [3]. This will require a sea change in the standard measurements of blood chemistries in clinical and hospital labs—where creatinine is routinely measured, but cystatin C is not. As these findings are implemented into routine clinical care, let’s hope they’ll reduce health disparities by leading to more accurate and timely diagnosis, supporting the goals of precision health and encouraging treatment of CKD for all people, regardless of their race.

References:

[1] Race, genetic ancestry, and estimating kidney function in CKD. Hsu CY, Yang W, Parikh RV, Anderson AH, Chen TK, Cohen DL, He J, Mohanty MJ, Lash JP, Mills KT, Muiru AN, Parsa A, Saunders MR, Shafi T, Townsend RR, Waikar SS, Wang J, Wolf M, Tan TC, Feldman HI, Go AS; CRIC Study Investigators. N Engl J Med. 2021 Sep 23.

[2] New creatinine- and cystatin C-based equations to estimate GFR without race. Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y, Crews DC, Doria A, Estrella MM, Froissart M, Grams ME, Greene T, Grubb A, Gudnason V, Gutiérrez OM, Kalil R, Karger AB, Mauer M, Navis G, Nelson RG, Poggio ED, Rodby R, Rossing P, Rule AD, Selvin E, Seegmiller JC, Shlipak MG, Torres VE, Yang W, Ballew SH,Couture SJ, Powe NR, Levey AS; Chronic Kidney Disease Epidemiology Collaboration. N Engl J Med. 2021 Sep 23.

[3] A unifying approach for GFR estimation: recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease. Delgado C, Baweja M, Crews DC, Eneanya ND, Gadegbeku CA, Inker LA, Mendu ML, Miller WG, Moxey-Mims MM, Roberts GV, St Peter WL, Warfield C, Powe NR. Am J Kidney Dis. 2021 Sep 22:S0272-6386(21)00828-3.

Links:

Chronic Kidney Disease (National Institute of Diabetes and Digestive and Kidney Diseases/NIH)

Explaining Your Kidney Test Results: A Tool for Clinical Use (NIDDK)

Chronic Renal Insufficiency Cohort Study

Chi-yuan Hsu (University of California, San Francisco)

Lesley Inker (Tufts Medical Center, Boston)

NIH Support: National Institute of Diabetes and Digestive and Kidney Diseases


Discussing Cancer Health Disparities

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Cancer Health Disparities
It was my pleasure to offer virtual remarks for the opening plenary session of the 13th Annual Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved Virtual Conference. The session was hosted by the American Association for Cancer Research (AACR) on October 2, 2020. I started things off speaking about factors that influence racial health disparities, including cancer disparities, and the need for greater workforce diversity. This videoconference image shows me with the panel members for the opening session. They are (starting top left to right) John Carpten, University of Southern California Keck School of Medicine, Los Angeles; Patricia LoRusso, Yale School of Medicine, New Haven, CT; Francis Collins; Brian Rivers, Morehouse School of Medicine; Atlanta; Chanita Hughes-Holbert, Medical University of South Carolina, Charleston; Mariana Stern, University of Southern California Keck School of Medicine, Los Angeles; Clayton Yates, Tuskegee University, AL; and Robert Winn, Virginia Commonwealth University Massey Cancer Center, Richmond.

Insurance Status Helps Explain Racial Disparities in Cancer Diagnosis

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Diverse human hands
Credit: iStock/jmangostock

Women have the best odds of surviving breast cancer if their disease is caught at an early stage, when treatments are most likely to succeed. Major strides have been made in the early detection of breast cancer in recent years. But not all populations have benefited equally, with racial and ethnic minorities still more likely to be diagnosed with later-stage breast cancer than non-Hispanic whites. Given that recent observance of Martin Luther King Day, I thought that it would be particularly appropriate to address a leading example of health disparities.

A new NIH-funded study of more than 175,000 U.S. women diagnosed with breast cancer from 2010-2016 has found that nearly half of the troubling disparity in breast cancer detection can be traced to lack of adequate health insurance. The findings suggest that improving insurance coverage may help to increase early detection and thereby reduce the disproportionate number of breast cancer deaths among minority women.

Naomi Ko, Boston University School of Medicine, has had a long interest in understanding the cancer disparities she witnesses first-hand in her work as a medical oncologist. For the study published in JAMA Oncology, she teamed up with epidemiologist Gregory Calip, University of Illinois Cancer Center, Chicago [1]. Their goal was to get beyond documenting disparities in breast cancer and take advantage of available data to begin to get at why such disparities exist and what to do about them.

Disparities in breast cancer outcomes surely stem from a complicated mix of factors, including socioeconomic factors, culture, diet, stress, environment, and biology. Ko and Calip focused their attention on insurance, thinking of it as a factor that society can collectively modify.

Many earlier studies had shown a link between insurance and cancer outcomes [2]. It also stood to reason that broad differences among racial and ethnic minorities in their access to adequate insurance might drive some of the observed cancer disparities. But, Ko and Calip asked, just how big a factor was it?

To find out, they looked to the NIH’s Surveillance Epidemiology, and End Results (SEER) Program, run by the National Cancer Institute. The SEER Program is an authoritative source of information on cancer incidence and survival in the United States.

The researchers focused their attention on 177,075 women of various races and ethnicities, ages 40 to 64. All had been diagnosed with invasive stage I to III breast cancer between 2010 and 2016.

The researchers found that a higher proportion of women receiving Medicaid or who were uninsured received a diagnosis of advanced stage III breast cancer compared with women with health insurance. Black, American Indian, Alaskan Native, and Hispanic women also had higher odds of receiving a late-stage diagnosis.

Overall, their sophisticated statistical analyses traced up to 47 percent of the racial/ethnic differences in the risk of locally advanced disease to differences in health insurance. Such late-stage diagnoses and the more extensive treatment regimens that go with them are clearly devastating for women with breast cancer and their families. But, the researchers note, they’re also costly for society, due to lost productivity and escalating treatment costs by stage of breast cancer.

These researchers surely aren’t alone in recognizing the benefit of early detection. Last week, an independent panel convened by NIH called for enhanced research to assess and explore how to reduce health disparities that lead to unequal access to health care and clinical services that help prevent disease.

References:

[1] Association of Insurance Status and Racial Disparities With the Detection of Early-Stage Breast Cancer. Ko NY, Hong S, Winn RA, Calip GS. JAMA Oncol. 2020 Jan 9.

[2] The relation between health insurance coverage and clinical outcomes among women with breast cancer. Ayanian JZ, Kohler BA, Abe T, Epstein AM. N Engl J Med. 1993 Jul 29;329(5):326-31.

[3] Cancer Stat Facts: Female Breast Cancer. National Cancer Institute Surveillance, Epidemiology, and End Results Program.

Links:

Cancer Disparities (National Cancer Institute/NIH)

Breast Cancer (National Cancer Institute/NIH)

Naomi Ko (Boston University)

Gregory Calip (University of Illinois Cancer Center, Chicago)

NIH Support: National Center for Advancing Translational Sciences; National Cancer Institute; National Institute on Minority Health and Health Disparities


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